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Scala Machine Learning Projects

You're reading from   Scala Machine Learning Projects Build real-world machine learning and deep learning projects with Scala

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788479042
Length 470 pages
Edition 1st Edition
Languages
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Author (1):
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Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Table of Contents (13) Chapters Close

Preface 1. Analyzing Insurance Severity Claims FREE CHAPTER 2. Analyzing and Predicting Telecommunication Churn 3. High Frequency Bitcoin Price Prediction from Historical and Live Data 4. Population-Scale Clustering and Ethnicity Prediction 5. Topic Modeling - A Better Insight into Large-Scale Texts 6. Developing Model-based Movie Recommendation Engines 7. Options Trading Using Q-learning and Scala Play Framework 8. Clients Subscription Assessment for Bank Telemarketing using Deep Neural Networks 9. Fraud Analytics Using Autoencoders and Anomaly Detection 10. Human Activity Recognition using Recurrent Neural Networks 11. Image Classification using Convolutional Neural Networks 12. Other Books You May Enjoy

Scala Play web service

As an application framework, Play2 was chosen as an easy-to-configure and robust framework. Compared to Spring (another popular framework), it takes less time to make a small app from scratch. The Play comes with Guice for dependency injection and SBT as the package manager:

  • Spark ML: The Spark ML library was chosen as it is one of the best-maintained libraries in the Java world. Many algorithms not available in the library itself are implemented by third-party developers and can be trained on top of Spark. A drawback of Spark is that it is quite slow, as by design it is supposed to be distributed; so it uses Hadoop and writes a lot into the filesystem.
  • Akka: This allows implementing the actor's pattern—having several instances of independent objects and passing messages to each other concurrently, which increases robustness.
  • Anorm: The...
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